Abstract
In this research work, the author proposes a new model of FrRNet-ERoI approach merely utilized to detect object within the remote sensing image. Here, we model a Faster R-CNN procedure comprise of network layer such as backbone ResNet-101 CNN network, HoG Feature Pyramid, Multi-scale rotated RPN and Enhanced RoI pooling network. To implement the proposed technique, the deep network containing respective layers which are trained through MATLAB software. In backbone layer, ResNet-101 network is preferred which effectively trained with the help of imagenet dataset. Then HoG feature pyramid inputted the final residual feature map to extract local informative features by image gradient approach. RPN poses to build an anchor box which is used to detect several numbers of objects. Finally, the enhanced RoI developed to optimize the model using bat algorithm. It performs several strategies to fine-tune the network. Because of this design approach, the proposed approach sustains to show high efficiency with reduced training time. The result concludes that proposed work achieves better detection accuracy than the several state-of-art techniques.
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References
Azimi SM, Vig E, Bahmanyar R, Körner M, Reinartz P (2018) Towards multi-class object detection in unconstrained remote sensing imagery. In: Asian conference on computer vision. Springer, Cham, pp 150–165
Bai X, Zhang H, Zhou J (2014) VHR object detection based on structural feature extraction and query expansion. IEEE Trans Geosci Remote Sens 52(10):6508–6520
Çağlar A, Aksoy S (2014) Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints. IEEE Trans Geosci Remote Sens 52(10):6627–6638
Chen X, Xiang S, Liu C-L, Pan C-H (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801
Chen Y, Gao J, Zhang K (2020) R-CNN-based satellite components detection in optical images. Int J Aerospace Eng
Cheng G, Han J (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11–28
Cheng G, Guo L, Zhao T, Han J, Li H, Fang J (2013) Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA. Int J Remote Sens 34(1):45–59
Cheng G, Zhou P, Han J (2016) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405–7415
Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
Guo Y, Liu Y, Oerlemans A, Lao S, Song W, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916
Khan MJ, Yousaf A, Javed N, Nadeem S, Khurshid K (2017) Automatic target detection in satellite images using deep learning. J Space Technol 7(1):44–49
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25:1097–1105
Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE Service Center, Piscataway, pp 2117–2125
Mahmoud AS, Mohamed S, El-Khoribi R, Abdelsalam H (2020) Object detection using adaptive mask RCNN in optical remote sensing images. Int J Intell Eng Syst 13:65–76
Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149
Ren Y, Zhu C, Xiao S (2018) Object detection based on fast/faster RCNN employing fully convolutional architectures. Math Probl Eng
Simonyan K, Zisserman A, (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Song Z, Sui H, Hua L (2021) A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and CNN. Int J Remote Sens 42(8):2827–2847
Uijlings JRR, Van De Sande KEA, Gevers T, Smeulders AWM (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171
Xia G-S, Bai X, Ding J, Zhu Z, Belongie S, Luo J, Datcu M, Pelillo M, Zhang L (2018) DOTA: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE Service Center, Piscataway, pp 3974–3983
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74
Yang X-S, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149
Yin S, Li H, Teng L (2020) Airport detection based on improved faster RCNN in large scale remote sensing images. Sensing Imaging 21(1):1–13
Zhang L, Shi Z, Jun W (2015) A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery. IEEE J Select Topics Appl Earth Observat Remote Sensing 8(10):4895–4909
Zhang F, Bo D, Zhang L, Miaozhong X (2016) Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Trans Geosci Remote Sens 54(9):5553–5563
Zhou P, Cheng G, Liu Z, Shuhui B, Xintao H (2016) Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping. Multidim Syst Sign Process 27(4):925–944
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Pazhani, A.A.J., Vasanthanayaki, C. Object detection in satellite images by faster R-CNN incorporated with enhanced ROI pooling (FrRNet-ERoI) framework. Earth Sci Inform 15, 553–561 (2022). https://doi.org/10.1007/s12145-021-00746-8
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DOI: https://doi.org/10.1007/s12145-021-00746-8